After decades of development, computer storage systems have already formed complete hierarchical structures. Technologies such as SRAMs, DRAMs, FLASH, EEPROMs and magnetic medium storage have respective positions in hierarchical structures according to different performance, so as to guarantee that a computing core has enough data for use during high-speed operation. Although performance and storage principles of various storage mediums are different, functions thereof are consistent, i.e., storing data to designated addresses and guaranteeing that the original data can be read out for use if the data are needed.
With the sharp increase of storage demands, processing capabilities of CPUs are enhanced stably with Moore' Law, construction scales of data centers become larger and larger, and consequently energy use costs gradually become the largest expenses of data centers. On the other hand, the expansion of data scales makes it more difficult to acquire useful information from data. Under this situation, numerous companies and researchers are focusing on neural networks of brains.
Human brains process a great amount of information by various means such as visual senses, auditory senses and tactile senses, and have matchless capabilities in aspects such as reasoning, recognition, association and prediction as compared with computer systems. However, as estimated, the power consumption of an adult brain is only about 20 W, and transmission speed of information in the brain can only reach a magnitude of milliseconds. The problem is how to improve operation modes of computer systems by obtaining an inspiration from the working modes of human brains to achieve the purpose of high-efficiency and low-power-consumption operation.
Neural network computation has already formed a very mature and complete theoretical system. Parallel processing mechanisms of neural networks of brains are imitated to form a multi-input multi-output system. This system has a more accurate prediction capability through the training of a great amount of data, and is realized in computer software in the beginning. In order to improve computing efficiency, more hardware systems realize hardware acceleration of algorithms through technologies such as processors and FPGAs (Field Programmable Gate Arrays). Further, IBM had launched an artificial Intelligence Watson computer system project many years ago, and led the research and development of the field of intelligent processing chips.
In the direction of imitating brains or imitating neural networks the research progress on storage technologies is much slower than computing technologies, and there is a long distance between the memory principle of human brain and the storage mode of computers. A fundamental difference lies in that human brains use objects and a logic relationship between objects as main memory content, but the capability in memorizing original information data such as images, sounds and characters is very weak. As a result, the thinking mode of human brains is greatly different from the computing mode of computers. After new storage technologies represented by phase change storage technology emerge, especially since the new storage technologies are based on resistive storage which not only is a nonvolatile storage technology but also supports high-speed random access, keen researchers have already started to try to manufacture storage chips which are closer to human brain memory by using these new technologies, expecting to realize application in the brain imitation or artificial intelligence field.